Singh Yashbir, Quaia Emilio
Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.
Department of Radiology, University of Padova, 35127 Padova, Italy.
Tomography. 2025 Jan 9;11(1):6. doi: 10.3390/tomography11010006.
This commentary examines Topological Data Analysis (TDA) in radiology imaging, highlighting its revolutionary potential in medical image interpretation. TDA, which is grounded in mathematical topology, provides novel insights into complex, high-dimensional radiological data through persistent homology and topological features. We explore TDA's applications across medical imaging domains, including tumor characterization, cardiovascular imaging, and COVID-19 detection, where it demonstrates 15-20% improvements over traditional methods. The synergy between TDA and artificial intelligence presents promising opportunities for enhanced diagnostic accuracy. While implementation challenges exist, TDA's ability to uncover hidden patterns positions it as a transformative tool in modern radiology.
本评论探讨了放射学成像中的拓扑数据分析（TDA），强调了其在医学图像解读方面的变革潜力。TDA基于数学拓扑学，通过持久同调与拓扑特征，为复杂的高维放射学数据提供了全新见解。我们探讨了TDA在医学成像领域的应用，包括肿瘤特征描述、心血管成像以及新冠病毒疾病（COVID-19）检测，在这些领域中，TDA相较于传统方法展现出了15%至20%的提升。TDA与人工智能之间的协同作用为提高诊断准确性带来了广阔机遇。尽管存在实施挑战，但TDA揭示隐藏模式的能力使其成为现代放射学中的变革性工具。
